Remaining useful life prediction of pressure regulating shutoff valve based on feature fusion and bidirectional gated recurrent unit

被引:0
作者
Meng, Ziran [1 ,2 ]
Zhu, Jun [1 ,2 ]
Yang, Lu [3 ]
Yu, Yang [3 ]
Huo, Yingdong [4 ]
Zhao, Zhibin [2 ]
机构
[1] Chongqing Univ, State Key Lab Mech Transmiss Adv Equipment, Chongqing 400044, Peoples R China
[2] Northwestern Polytech Univ, Sch Civil Aviat, Xian 710060, Peoples R China
[3] COMAC Shanghai Aircraft Custom Serv Co Ltd, Shanghai 200241, Peoples R China
[4] Jiangxi Air Co Ltd, Dept Maintenance Engn, Nanchang 33000, Peoples R China
基金
中国博士后科学基金; 中国国家自然科学基金;
关键词
remaining life prediction; feature fusion; BiGRU; PRSOV; NEURAL-NETWORKS; PROGNOSTICS; MODEL;
D O I
10.1088/1361-6501/ad9bd6
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In the bleed air system of the ARJ21 aircraft, pressure regulating shutoff valve (PRSOV) failures are common, and their failures can lead to disasters and economic losses. Accordingly, prediction of the degradation performance of PRSOV is crucial. This work proposes a life prediction method based on principal component analysis (PCA) and bidirectional gated recurrent units (BiGRUs) to achieve accurate prediction. After obtaining pressure data throughout the entire life of PRSOV, considering that the pressure required for PRSOV during the takeoff and climb phases is the most critical, data from this phase are selected for focused monitoring. Classical statistical feature extraction methods are used to extract features from the raw pressure data during the takeoff and climb phases. An empirical feature extraction method with low-pressure weighting is also proposed based on engineering practical experience. Feature fusion is performed using PCA based on these two types of features. Finally, BiGRU is utilized to model the fused degradation feature indicators and estimate the remaining service life of PRSOV. The results of the analysis of the full life data of PRSOV in ARJ21 aircraft indicated that the proposed method can effectively predict its remaining service life. The proposed method demonstrated higher prediction accuracy compared with the related prediction methods.
引用
收藏
页数:13
相关论文
共 50 条
  • [41] A Baseline Similarity Attention-Based Dual-Channel Feature Fusion Network for Machine Remaining Useful Life Prediction
    Hu, Yawei
    Li, Xuanlin
    Wang, Hang
    Liu, Yongbin
    Liu, Xianzeng
    Cao, Zheng
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 12
  • [42] Two-stage Remaining Useful Life Prediction Based on the Wiener Process With Multi-feature Fusion and Stage Division
    Guan, Qingluan
    Zuo, Zhongyi
    Teng, Yanqin
    Zhang, Huixian
    Jia, Limin
    EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY, 2024, 26 (04):
  • [43] Remaining useful life prediction of bearings based on multiple-feature fusion health indicator and weighted temporal convolution network
    Wang, Huaqing
    Zhang, Xisen
    Guo, Xudong
    Lin, Tianjiao
    Song, Liuyang
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (10)
  • [44] Remaining useful life prediction of PEMFC based on long short-term memory recurrent neural networks
    Liu, Jiawei
    Li, Qi
    Chen, Weirong
    Yan, Yu
    Qiu, Yibin
    Cao, Taiqiong
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2019, 44 (11) : 5470 - 5480
  • [45] PFFN: A Parallel Feature Fusion Network for Remaining Useful Life Early Prediction of Lithium-Ion Battery
    Dong, Zhekang
    Yang, Mengjie
    Wang, Junfan
    Wang, Hao
    Lai, Chun Sing
    Ji, Xiaoyue
    IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, 2025, 11 (01): : 2696 - 2706
  • [46] Multi-sensor information fusion-based prediction of remaining useful life of nonlinear Wiener process
    Wu, Bin
    Zeng, Jianchao
    Shi, Hui
    Zhang, Xiaohong
    Shi, Guannan
    Qin, Yankai
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2022, 33 (10)
  • [47] Bearing remaining useful life prediction of fatigue degradation process based on dynamic feature construction
    Zhu, Hongqiu
    Huang, Ziyi
    Lu, Biliang
    Zhou, Can
    INTERNATIONAL JOURNAL OF FATIGUE, 2022, 164
  • [48] A novel remaining useful life prediction method based on gated attention mechanism capsule neural network
    Zhao, Chengying
    Huang, Xianzhen
    Li, Yuxiong
    Li, Shangjie
    MEASUREMENT, 2022, 189
  • [49] Gated Transient Fluctuation Dual Attention Unit Network for Long-Term Remaining Useful Life Prediction of Rotating Machinery Using IIoT
    Li, Shuaiyong
    Zhang, Chao
    Liu, Liang
    Zhang, Xuyuntao
    IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (10): : 18593 - 18604
  • [50] Dimensionality reduce-based for remaining useful life prediction of machining tools with multisensor fusion
    Zhu, Yongmeng
    Wu, Jiechang
    Wu, Jun
    Liu, Shuyong
    RELIABILITY ENGINEERING & SYSTEM SAFETY, 2022, 218